What Are the Components of a Sharded Cluster in MongoDB?

A sharded cluster in MongoDB is composed of 3 elements: shards, config servers, and mongos. These are the following.


Data from a sharded cluster is encompassed in a subset which is stored in a shard. All shards grouped together to maintain the data of the entire cluster. For deployment, you have to configure a shard as a replica set in order to achieve high availability and redundancy. Shards are used for maintenance and administrative operations.

Primary Shard

All the databases have their own primary shard. Primary shards reside in a sharded cluster’s database. They store all of those collections which are not sharded. Bear in mind, that there is no link between the primary shard and the primary replica member of the replica set, hence do not be confused by the similarity in their names.

A primary shard is chosen by the mongos during the generation of a new database. To choose a shard, mongos picks the one which contains minimum data.

It is possible to change the primary shard via a command, “movePrimary”. However, do not change your primary member so casually. The change of a primary shard is a time-consuming procedure. During the primary shard migration, you cannot use collections of that shard’s database. Likewise, cluster operations can be disrupted, and the extent of this disturbance is reliant on the data which is currently migrating.

To check a general view of the cluster in terms of sharding, you can use the sh.status method via the mongo shell. The results generated by this method also specify the primary shard of the database. Other useful information includes information about how the chunks are distributed among the shards.

Config Servers

The sharded cluster’s metadata is stored in the config servers. This metadata can include the organization of the components in the cluster along with the states of these components and data. Information about all the shards and their chunks are maintained in the config servers.

This type of data is then used for caching by the mongos instances after which it is used for routing associated with read and writes operations with respect to the appropriate shards. When new metadata is updated, then the cache is also updated by the mongos.

It must be noted that the configuration of “authentication” in MongoDB like internal authentication and RBAC (role-based access control) is also stored in these config servers. Additionally, MongoDB utilizes them for the management of distributed locks.

While it is possible for a single config server to be used with all the sharded clusters, such practice is not recommended. If you have multiple sharded clusters, then use a separate config server for all of them.

Config Servers and Read/Write Operations

Write Operations

If you are a MongoDB user, then you must be familiar with the admin and config databases in MongoDB. Both of these databases are maintained in the config servers. Collections associated with the authorization, authentication, and system collections are stored by the “admin” database. On the other hand, the metadata of the sharded cluster is stored in the “config” database.

When the metadata is modified like when a chunk is split or a chunk is migrated, then MongoDB directs write operations on the config DB. During these write operations, MongoDB uses “majority” for the write concern.

However, as a developer, you should refrain from writing to the config DB by yourself in the midst of maintenance or standard operations.

Read Operations

The admin database is used for read operations by the MongoDB. These reads are associated with authorization, authentication, and internal operations.

If the metadata is modified like when a chunk is being migrated or mongos is initiated, then read operations are processed to the config database by the MongoDB. MongoDB uses the “majority” read concern during these read operations. Moreover, it is not only the MongoB which reads from config servers. Shards also require them for read operations associated with the metadata of the chunks.


The mongos instances in MongoDB are responsible for shard related write operations while they are also utilized for the routing of queries. For the sharded cluster, mongos is the only available interface which offers the application perspective. Keep this in mind that there is no direct communication between the shards and applications.

Mongos monitors the contents of a shard via metadata caching with the help of the config servers. The metadata provided by the config servers is then used by the MongoDB for routing operations between clients and applications with the instances of the mongod. Mongos instances lack a persistent state. This is helpful because it assists in the lowest possible consumption of the available resources.

Usually, mongos instances are run on a system which also houses the applications servers. However, if your situation demands, then you can also use them on any other dedicated resources like shards.

Routing and Results

While routing a query for a cluster, a mongos instance assesses all the shards through a list and identifies which shard needs the query. It then forms a cursor for all of these specific shards.

Afterward, the data in these shards is merged by the mongos instance which is then displayed in the result document. There are some modifiers for queries like sorting which maybe required to be executed on a shard. Subsequently, the retrieval of the results is carried out by the mongos. To manage the query modifiers, mongos does the following.

  • In the case of un-sorted query results, mongos applies a “round-robin” strategy for the generation of results from the shards.
  • In the scenario in which the result size is restricted because of the limit() method, then shards receive this information from the mongos. Afterward, they re-implement limit on the result and then send it to the client.

If the skip() method is used in the query, then like the previous case, it is not possible for mongos to forward the information. Instead, it searches the shards and fetches the unskipped results after which the specified skip limit is processed during the arrangement of the entire result.


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